Coarse‐Scale Distribution Surveys and Occurrence Probability Modeling for Wolverine in Interior Alaska
Bibliographic record
Abstract
ABSTRACT We determined wolverine ( Gulo gulo ) distribution and occurrence probabilities using aerial surveys and hierarchical spatial modeling in a 180,000‐km 2 portion of Interior Alaska, USA. During 8 February‐12 March 2006, we surveyed 149 of 180 1,000‐km 2 sample units for wolverine tracks. We observed wolverine tracks in 99 (66.4%) sample units. Wolverine detection probability was ≥69% throughout the survey period. Posterior occurrence probabilities of whether a wolverine track occurred in a sample unit was dependent on survey timing, number of transects flown, number of neighboring sample units with detected tracks, percentage of the sample unit with elevation ≤305 m, and human influences. Our model indicated strong evidence of occurrence (>0.80) in 72% of the 180 survey units, strong evidence of absence (<0.20) in 12%, and weak evidence of occurrence or absence (0.20–0.80) in 16%. Wolverine area of occupancy made up 83% of the study area. Simulations illustrated that 2–4 survey routes were necessary for the survey technique to provide strong evidence of wolverine presence or absence in Interior Alaska if a track was not identified along the first route. The necessary number of survey routes depends on the occurrence probability in a sample unit. We provided managers with a map of wolverine distribution in Interior Alaska and an efficient and lower‐cost method to detect coarse‐scale changes in wolverine distribution. Our technique was effective in both Interior Alaska and Ontario, Canada, suggesting it would be effective throughout most of the boreal forest range of wolverines where tracks can be readily observed from the air. The technique requires a certain skill level in recognizing tracks; it is essential that tracks are identified correctly and training may be necessary depending on surveyor experience.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".